Generalized Cross Correlation Research Articles

Shooter Localization based on DoA Estimation of Gunshot Signals and Digital Map Information

This paper discusses a technique to estimate the position of a sniper using a spatial microphone array placed on elevated platforms. The shooter location is obtained from the exact location of the microphone array, from topographic information of the area and from an estimated direction of arrival (DoA) of the acoustic wave related to the explosion in the gun barrel, which is known as muzzle blast. The estimation of the DOA is based on the time differences the sound wavefront arrives at each pair of microphones, employing a technique known as Generalized Cross Correlation (GCC) with phase transform. The main idea behind the localization procedure used herein is that, based on the DoA, the acoustical path of the muzzle blast (from the weapon to the microphone) can be marked as a straight line on a terrain profile obtained from an accurate digital map, allowing the estimation of the shooter location whenever the microphone array is located on an dominant position. In addition, a new approach to improve the DoA estimation from a cognitive selection of microphones is introduced. In this technique, the microphones selected must form a consistent (sum of delays equal to zero) fundamental loop. The results obtained after processing muzzle blast gunshot signals recorded in a typical scenario, show the effectiveness of the proposed method.

Three-dimensional-positioning based on echolocation using a simple iterative method

This paper describes a method for determining the position of an object in three dimensions using time-of-flight (TOF) echolocation. The position is computed using a one-bit cross correlation and requires less computation time than a general cross correlation. A delta-sigma modulation board is introduced to convert the echo signal into one-bit signals. This solution uses an iterative method, i.e., Newton–Raphson, to obtain a position estimate. Additionally, an initial value for the solution is obtained by averaging TOFs, and no guess is required. Three hand-held communication devices are adapted as acoustical receivers. The validity of the proposed method is verified by the repeatability of the experimental results.

Subband processing‐based approach for the localisation of two simultaneous speakers

Time difference of arrival (TDOA)-based techniques are a main category of speaker localisation methods. In a large subcategory of these methods, the generalised cross-correlation (GCC) is employed for TDOA estimation. In this study, the authors propose a subband processing-based method that computes the GCC of the microphone pairs in each subband. The information collected from different subbands is then combined together to estimate the direction of two simultaneous speakers. While the conventional methods consider the whole signal spectrum in the localisation procedure, the proposed method takes advantage of the difference in the frequency contents of the speakers. The proposed method computes the histograms of the peak positions of the GCC curve for each microphone pair in different subbands. These histograms are then fused using one of the three proposed histogram averaging methods, called simple, sectional, and weighted averaging. The proposed method has been evaluated on simulated and real speech data in noisy, reverberant, and noisy–reverberant conditions. The evaluation results demonstrate the superiority of the proposed subband processing-based method over its full-band counterpart. The authors’ experiments also show that among different histogram averaging methods, the weighted averaging has greater performance in estimating the direction of speakers.

Distributed Kalman filter-based speaker tracking in microphone array networks

Using a microphone array network, a speaker tracking method based on distributed Kalman filter (DKF) in a noisy and reverberant environment is proposed. Firstly, the time delay of arrival (TDOA) in each microphone pair is estimated by the generalized cross-correlation (GCC) method. Next, the Langevin model is used as state equation to model the speaker’s movement, meanwhile the measurement equations with true TDOA are deduced by linearizing the TDOA model. Finally, the moving speaker’s positions are estimated by distributed Kalman filtering in a microphone array network. The proposed method is scalable. It can obtain a trajectory of the speaker’s movement smoothly with excellent tracking accuracy. Simulation results verify the effectiveness of the proposed method.

Design of a Pipeline Leak Detection System

This paper develops a pipeline leak detection software based on the vibration analysis method. It introduces the hardware designs, experimental methods and the based theory, experimental methods, and the leak detection algorithm. The system uses the generalized cross-correlation (GCC) algorithm to analyze those signals including vibration, temperature, flow and pressure. Then the system takes use of matlab to analyze those signals to choose the best weighted methods. At last, the software uses LabVIEW to generate the fronted user interface. Key words: Leak detection; Generalized Cross-correlation; Vibration analysis; LabVIEW

MUSIC 및 반향 성분 제거 기법을 이용한 음성신호의 입사각 추정

In this paper, we propose a method to improve the performance of the direction-of-arrival (DOA) estimation of a speech source using a multiple signal classification (MUSIC)-based algorithm. Basically, the proposed algorithm utilizes a complex coefficient band pass filter to generate the narrow band signals for signal analysis. Also, reverberation component reduction and quadratic function-based response approximation in MUSIC spatial spectrum are utilized to improve the accuracy of DOA estimation. Experimental results show that the proposed method outperforms the well-known generalized cross-correlation (GCC)-based DOA estimation algorithm in the aspect of the estimation error and success rate, respectively.Abstract should be placed here. These instructions give you guidelines for preparing papers for JICCE.

Improved sound source localization in horizontal plane for binaural robot audition

An improved sound source localization (SSL) method has been developed that is based on the generalized cross-correlation (GCC) method weighted by the phase transform (PHAT) for use with binaural robots equipped with two microphones inside artificial pinnae. The conventional SSL method based on the GCC-PHAT method has two main problems when used on a binaural robot platform: 1) diffraction of sound waves with multipath interference caused by the contours of the robot head, which affects localization accuracy, and 2) front-back ambiguity, which limits the localization range to half the horizontal space. The diffraction problem was overcome by incorporating a new time delay factor into the GCC-PHAT method under the assumption of a spherical robot head. The ambiguity problem was overcome by utilizing the amplification effect of the pinnae for localization over the entire azimuth. Experiments conducted using two dummy heads equipped with small or large pinnae showed that localization errors were reduced by 8.91° (3.21° vs. 12.12°) on average with the new time delay factor compared with the conventional GCC-PHAT method and that the success rate for front-back disambiguation using the pinnae amplification effect was 29.76 % (93.46 % vs. 72.02 %) better on average over the entire azimuth than with a conventional head related transfer function (HRTF)-based method.

Interaural Time Difference Estimation Using Generalized Crosscorrelation with Maximum Likelihood Weighting in Reverberant Environments

In this paper, an interaural time difference (ITD) estimation method is proposed for binaural speech separation in reverberant environments. First, the auditory signals are represented in the time-frequency (T-F) domain, and the ITD for each T-F bin is then estimated using generalized cross-correlation (GCC) with a maximum likelihood (ML) weighting function. In particular, the ML weighting function is designed to reduce the reverberation effect. Then, a mask is estimated by comparing the estimated ITD with the ITD corresponding to the location of the pre-defined target speech source. Finally, the target speech is separated by applying the mask to the auditory signals. It is shown that the proposed ITD estimation method outperforms a conventional cross-correlation-based ITD estimation method under reverberant conditions in terms of the signal-to-noise ratio (SNR) and signal-to-distortion ratio (SDR) of the separated speech signals.

Real-time Sound Localization Using Generalized Cross Correlation Based on 0.13 ㎛ CMOS Process

In this paper, we present the design and implementation of real-time sound localization based on 0.13 m CMOS process. Time delay of arrival (TDOA) estimation was used to obtain the direction of the sound signal. The sound localization chip consists of four modules: data buffering, short-term energy calculation, cross correlation, and azimuth calculation. Our chip achieved real-time processing speed with full range (360°) using three microphones. Additionally, we developed a dedicated sound localization circuit (DSLC) system for measuring the accuracy of the sound localization chip. The DSLC system revealed that our chip gave reasonably accurate results in an experiment that was carried out in a noisy and reverberant environment. In addition, the performance of our chip was compared with those of other chip designs.

Design of a Vibration-Based Pipeline Leak Detection System

This paper develops a pipeline leak detection system based on vibration analysis method. It introduces hardware designs, experimental methods and diagnosis algorithms. The system uses the generalized cross-correlation (GCC) algorithm to analyze the vibration signals stimulated by pipeline leakage. And the analysis is weighted by three methods, including ROTH, SCOT and PHAT method. The results show that this system can quickly and accurately detect the pipeline leakage.

Seismic while drilling experiment with diamond drilling at Brukunga, South Australia

Seismic-While-Drilling (SWD) utilises drill bit vibrations as a seismic source and receivers at the surface or in a borehole to acquire reverse VSP data. The basic processing technique is based on cross correlation to generate active shot-gather-like profiles. The successful implementation of SWD will yield time-depth information and image around the drill bit, which can aid drilling and geological understanding of the area. To study the feasibility of using diamond impregnated drill bits for seismic-while-drilling,we conducted a small pseudo 3D SWD experimentat Brukunga, South Australia. It has been used to investigate the signals generated from diamond drilling, and study the potential to use a drill bit as a seismic source. The drill bit energy for seismic imaging is influenced by the rig power setting, and the state of the drill bit (new or worn bit). The experiment shows that normally the diamond drilling frequency band is wide with strong discrete peaks,however sometimes due to changes in the drilling mode, e.g., increase or decrease the drilling power, the frequency spectrum can be smoothed. The strong peaks in the spectrum mean strong periodicity of the signal and as such, the signals do not lend themselves to standard cross correlation. Thus, we use a generalised cross correlation that produces results similar to an active shot gather-like profile.

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

This paper focuses on road traffic monitoring using sounds and proposes, more specifically, a microphone array design methodology for observing vehicle trajectory from acoustic-based correlation functions. In a former work, authors have shown that combining generalized cross correlation (GCC) functions and a particle filter onto the audio signals simultaneously acquired by two sensors placed near the road allows the joint estimation of the speed and the wheelbase length of road vehicles as they pass by. This is mainly due to the broadband nature of the tire/road noise, which makes their spatial dissociation possible by means of an appropriate GCC processor. At the time, nothing has been said about the best distance to chose between the sensors. A methodology is proposed here to find this optimum, which is expected to improve the observation quality and, thus, the tracking performance. Theoretical developments of this paper are partially assessed with preliminary experiments.

A Two Seismic Sensor Based Approach for Moving Vehicle Detection

A novel method for using roadside seismic (accelerometer) sensors to detect moving vehicles driven in traffic lanes is investigated in this paper. The method uses two seismic sensors installed on a road shoulder, with the recorded seismic signals split into fixed time intervals. The time delay of arrival (TDOA) is estimated by use of a generalized cross-correlation (GCC) approach. Further estimation methods can then be used to characterize various kinds of vehicle information, including vehicle speed, axle spacing, and driving direction. The accuracy of the approach is investigated using experimental results obtained from an urban street environment.

AdaBoost based EMD as a De-Noising Technique in Time Delay Estimation Application

Estimation of time delay between signals received at two spatially separated sensors has considerable practical importance in the applications like source localization, direction finding etc., in RADAR, SONAR and other communication systems. In this paper cross correlation (CC) generalized cross correlation with phase transform (GCCPHAT) and maximum likelihood (ML) estimation methods are used as the time delay estimation methods. Prior to the delay estimation, the received signals are de-noised by AdaBoost based EMD technique. The performance of the delay estimation is significantly degraded by the signal-tonoise ratio (SNR) level and hence this factor has been considered as a principal factor. The simulation results of the proposed method are compared with the basic EMD as a denoising technique at various SNR levels. The results show that the proposed method improves the resolution in the delay estimation in the noisy environment.

Research on Sound Source Localization System Based on Time Delay Estimation

In the estimation of time delay, there always would not appear obvious peak with the basic cross-correlation (CC). In order to solve the problem of the basic cross-correlation method, this essay represents an improved time delay estimation method based on the generalized cross-correlation (GCC) and combines with the microphone array structure to achieve sound source localization. Finally, the simulation results show that this method could measure the sound source’s location accurately with noise and reverberation, and the distance positioning error is less than 10cm, the direction angle error is below 3°.

Passive acoustic localisation using blind Gauss–Markov estimate with spectral estimation at each sensor

Time-delay estimation has essential applications in the field of radar, sonar and robotics. For a very distant source, time-delay vector estimation across an M-sensor array is realised using the generalised cross-correlation (GCC) function and the estimates combined with their covariance matrix, expressed in terms of a priori known signal and noise spectra, to yield a linear minimum variance unbiased estimator, known as the Gauss–Markov Estimate. In the absence of a priori information, spectral estimation has to be done at one of the sensors. For close range sources, the use of amplitude attenuation information across the array will improve this estimate further. This study presents a new amplitude information related Gauss–Markov estimate, which calculates the power spectral density (PSD) at all the sensors of the array and gives more accurate time delay estimates in terms of the mean-square error when compared to the earlier constant amplitude-based technique using the PSD at the closest sensor. The performance has been evaluated against signal-to-noise ratio for varying distance of a source from the receiving array. The results have been verified by simulations and experiments for a two-dimensional source localisation problem.

Improved binaural sound localization and tracking for unknown time-varying number of speakers

A method based on the generalized cross-correlation (GCC) method weighted by the phase transform (PHAT) has been developed for binaural sound source localization (SSL) and tracking of multiple sound sources. Accurate binaural audition is important for applying inexpensive and widely applicable auditory capabilities to robots and systems. Conventional SSL based on the GCC-PHAT method is degraded by low resolution of the time difference of arrival estimation, by the interference created when the sound waves arrive at a microphone from two directions around the robot head, and by impaired performance when there are multiple speakers. The low-resolution problem is solved by using a maximum-likelihood-based SSL method in the frequency domain. The multipath interference problem is avoided by incorporating a new time delay factor into the GCC-PHAT method with assuming a spherical robot head. The performance when there are multiple speakers was improved by using a multisource speech tracking method consisting of voice activity detection (VAD) and K-means clustering. The standard K-means clustering algorithm was extended to enable tracking of an unknown time-varying number of speakers by adding two additional steps that increase the number of clusters automatically and eliminate clusters containing incorrect direction estimations. Experiments conducted on the SIG-2 humanoid robot show that this method outperforms the conventional SSL method; it reduces localization errors by 18.1° on average and by over 37° in the side directions. It also tracks multiple speakers in real time with tracking errors below 4.35°.

Real-time sound source localization using hybrid framework

Real-time sound source localization with microphone array still remains as a difficult task. The steered response power–phase transform (SRP–PHAT) method is proved to be robust, but it suffers from its high computational cost. In this paper, we propose a hybrid framework for real-time sound source localization with microphone array. The main concept is using the results of generalized cross correlation (GCC) based time difference of arrival (TDOA) estimation to narrow down the search space of SRP–PHAT. A circular clustering algorithm is developed to interlink the above two parts and calculate the extracted search space. The proposed hybrid method maintains the high localization accuracy of traditional SRP–PHAT algorithm, and the computational cost is much reduced. Moreover, any existing improvement of SRP algorithm can be directly incorporated into the hybrid framework to further improve performance. Experiment and simulation results validate the efficiency and accuracy of the proposed method.

Improved estimation of direction of arrival of sound sources for hearing aids using gyroscopic information

Determining the direction of arrival (DOA) of a sound source is important in spatial audio signal processing, as it can lead to substantial improvement in noise reduction performance. Techniques such as generalized cross correlation with phase transform (GCC-PHAT) and adaptive eigenvalue decomposition (AED) perform optimally when the measurement microphones are fixed in place. However, hearing-aid microphones move with the listener’s head movements, which can result in momentarily inaccurate directional estimates and noise artifacts in the output signal. Techniques such as GCC-PHAT experience degraded short-term performance in the presence of multiple signals and noise. The system presented measures instantaneous head movement velocity using a micro-electromechanical systems (MEMS) gyroscope attached to binaurally communicating hearing aids. Estimates of DOA for physically stationary sources are shifted based on the gyroscope's head-movement information. Using GCC-PHAT with gyroscopic input can produce robust in situ DOA estimates for several sources in reverberant environments. In addition, the gyroscope allows an adaptive beamformer to be steered to a target direction, compensating for head movements on a very short timescale during DOA estimation. Results show improved localization performance over a standard GCC-PHAT system during head movements.

U-TDOA Position Location Technique for WCDMA

Position location in cellular networks has become increasingly important in recent years due to emerging of location-based services. Numerous applications use position location such as emergency calls, network optimization to resource management and improve the performance of handover in cellular network.U plink Time Difference of Arrival (U- TDOA) technique in cellular networks is dealt with this technique based on estimating the time difference between the received signal to the base stations of a certain subscriber using the Generalized Cross Correlation method. Chan’s method is used in solving the resulting hyperbolic equations for finding the Position Location (PL) of a certain subscriber. Chan’s method give accurate results, compared with other techniques .in this paper we concern on the some factors (increase sample, increase power without and with interference cancellation) for improved U-TDOA Accuracy.